The smart grid concept continues to evolve and various methods have been developed to enhance the energy efficiency of the electricity infrastructure. Demand Response (DR) is considered as the most cost-effective and reliable solution for the smoothing of the demand curve, when the system is under stress. DR refers to a procedure that is applied to motivate changes in the customers' power consumption habits, in response to incentives regarding the electricity prices. In this paper, we provide a comprehensive review of various DR schemes and programs, based on the motivations offered to the consumers to participate in the program. We classify the proposed DR schemes according to their control mechanism, to the motivations offered to reduce the power consumption and to the DR decision variable. We also present various optimization models for the optimal control of the DR strategies that have been proposed so far. These models are also categorized, based on the target of the optimization procedure. The key aspects that should be considered in the optimization problem are the system's constraints and the computational complexity of the applied optimization algorithm.

In this work, the concept of dual-band resistance compression networks is introduced and applied to the design of rectifier circuits with improved performance. The use of resistance compression networks (RCNs) minimizes the sensitivity of rectifier circuits to variations in the surrounding environment, such as input power level and changes in the rectifier load. The proposed dual-band RCN can be used as the matching network located between the antenna and the rectifying element of a dual-band rectifier for energy harvesting applications. A dual-band ( 915 MHz /2.45 GHz) rectifier based on RCN is designed and characterized showing improved performance in comparison with a conventional dual-band envelope detector by exhibiting improved RF-dc conversion efficiency and reduced sensitivity versus output load and input power variations.

Electrification of transportation is considered as one of the most promising ways to mitigate climate change and reduce national security risks from oil and gasoline imports. Fast charging stations that provide high quality of service will facilitate the wide market penetration of electric vehicles. In this paper, the operation of a fast charging station is analyzed by employing a novel queuing model. The proposed analysis considers that the various electric vehicle models are classified by their battery size, and computes the customers' mean waiting time in the queue by taking into account the available charging spots, as well as the stochastic arrival process and the stochastic recharging needs of the various electric vehicle classes. Furthermore, a charging strategy is proposed according to which the drivers are motivated to limit their energy demands. The implementation of the proposed strategy allows the charging station to serve more customers without any increase in the queue waiting time. The high precision of the present analytical model is confirmed through simulations. Therefore, it may be utilized by existing fast charging station operators that need to provide high quality of service, or by future investors that need to design an efficient installation

In this paper we propose novel and more realistic analytical models for the determination of the peak demand under four power demand control scenarios. Each scenario considers a finite number of appliances installed in a residential area, with diverse power demands and different arrival rates of power requests. We develop recursive formulas for the efficient calculation of the peak demand under each scenario, which take into account the finite population of the appliances. Moreover, we associate each scenario with a proper real-time pricing process in order to derive the social welfare. The proposed analysis is validated through simulations. Moreover, the performance evaluation of the proposed formulas reveals that the absence of the assumption of finite number of appliances could lead to serious peak-demand over-estimations

Electric vehicles (EVs) have been considered as a feasible solution to deal with the high fuel consumption and greenhouse gas emissions caused by conventional vehicles. However, long charging times and drivers' range anxiety are the main disadvantages of EVs. A key factor that is expected to mitigate these problems and facilitate the wide adoption of EVs will be the effective operation of fast charging stations (FCSs). In this paper, the operation of a FCS is evaluated, in terms of operator's profits and customers' waiting time in the queue. The FCS contains both dc and ac outlets that provide high power levels, while the various EV models are classified by their battery size and the fast charging option they use (dc or ac). The operator's daily profits and the queue waiting time are initially computed by considering that the EVs recharge under a flat-rate pricing policy. In order to avoid a long queue build-up at the FCS, a new pricing policy is then proposed. The intuition behind the scheduled pricing policy is that users are deterred to charge more than an arranged energy threshold, thus reducing the load and the waiting time at the FCS.

Smart homes that contain renewable energy sources, storage systems, and controllable loads will be key components of the future smart grid. In this article, we develop a reinforcement-learning (RL)-based scheme for the real-time energy management of a smart home that contains a photovoltaic system, a storage device, and a heating, ventilation, and air conditioning (HVAC) system. The objective of the proposed scheme is to minimize the smart home’s electricity cost and the residents’ thermal discomfort by appropriately scheduling the storage device and the HVAC system on a daily basis. The problem is formulated as a Markov decision process, which is solved using the deep deterministic policy gradient (DDPG) algorithm. The main contribution of our study compared to the existing literature on RL-based energy management is the development of a clustering process that partitions the training data set into more homogeneous training subsets. Different DDPG agents are trained based on the data included in the derived subsets, while in real time, the test days are assigned to the appropriate agent, which is able to achieve more efficient energy schedules when compared to a single DDPG agent that is trained based on a unified training data set.